Wearable Monitoring System Research Articles

Development of a Remote Health-Monitoring System for Frail People: A Preliminary Study.

Respiratory and cardiovascular functions decline with age in elderly individuals. Consequently, the incidence of chronic respiratory and cardiovascular diseases increases with age. Heart disease and pneumonia are the leading causes of death in Japan. Given the pathophysiological nature of these diseases, patients inevitably require monitoring of their cardiac and pulmonary functions, such as heart rate and arterial blood oxygenation, as systemic parameters. In addition, monitoring skin temperature and humidity as local parameters is preferable for elderly individuals to maintain healthy daily conditions. In the present study, we developed a wearable vital sign monitoring system and validated the accuracy of the device under development as compared to authorised medical devices that measure these systemic and local parameters in the peripheral tissue of the palm. For the systemic parameters, mean values showed no significant differences between the two devices, but the data bias was greater for the device under development. For the local parameters, mean values showed significant differences between the two devices; however, the data bias was the same for both devices. The acceptable data acquisition of the device under development was approximately 89%, with error acquisition mainly caused by the measurement of systemic parameters. We conclude that further improvements in measurement of systemic parameters are required to increase the data acquisition beyond 90%.

Functional fibers/textiles for smart sensing devices and applications in personal healthcare systems.

Personalized medical diagnostics and monitoring have become increasingly important due to inefficient and delayed medical services of traditional centralized healthcare systems. To enhance the comfort and portability, flexible health monitoring systems have been developed in recent years. In particular, smart fiber/textile-based sensing devices show superiority for continuously monitoring personal health and vital physiological parameters owing to their light weight, good flexibility and inherent miniaturization. This review focuses on the recent advances in smart fiber/textile-based sensing devices for wearable electronic applications. First, fabrication strategies of smart sensing fibers/textiles are introduced in detail. In addition, sensing performances, working principles and applications of smart sensing fibers/textiles such as pressure sensing fibers/textiles, stretchable strain sensing fibers/textiles, temperature sensing fibers/textiles, and biofluid, gas and humidity sensing fibers/textiles in health monitoring are also reviewed systematically. Finally, we propose current challenges and future prospects in the area of fiber/textile-based sensors for wearable healthcare monitoring and diagnosis systems. In general, this review aims to give an overall perspective of the promising field by reviewing various fiber/textile-based sensing devices and highlighting the importance for researchers to keep up with the sequential exploration of soft sensing fibers/textiles for applications in wearable smart systems.

A Discolorable Flexible Synaptic Transistor for Wearable Health Monitoring.

Multifunctional intelligent wearable electronics, providing integrated physiological signal analysis, storage, and display for real-time and on-site health status diagnosis, have great potential to revolutionize health monitoring technologies. Advanced wearable systems combine isolated digital processor, memory, and display modules for function integration; however, they suffer from compatibility and reliability issues. Here, we introduce a flexible multifunctional electrolyte-gated transistor (EGT) that integrates synaptic learning, memory, and autonomous discoloration functionalities for intelligent wearable application. This device exhibits synergistic light absorption coefficient changes during voltage-gated ion doping that modulate the electrical conductance changes for synaptic function implementation. By adaptively changing color, the EGT can differentiate voltage pulse inputs with different frequency, amplitude, and duration parameters, exhibiting excellent reversibility and reliability. We developed a smart wearable monitoring system that incorporates EGT devices and sensors for respiratory and electrocardiogram signal analysis, providing health warnings through real-time and on-site discoloration. This study represents a significant step toward smart wearable technologies for health management, offering health evaluation through intelligent displays.

Smart Home Sleep Respiratory Monitoring System Based on a Breath-Responsive Covalent Organic Framework.

A smart home sleep respiratory monitoring system based on a breath-responsive covalent organic framework (COF) was developed and utilized to monitor the sleep respiratory behavior of real sleep apnea patients in this work. The capacitance of the interdigital electrode chip coated with COFTPDA-TFPy exhibits thousands-level reversible responses to breath humidity gases, with subsecond response time and robustness against environmental humidity. A miniaturized printed circuit board, an open-face-mask-based respiratory sensor, and a smartphone app were constructed for the wearable wireless smart home sleep respiratory monitoring system. Leveraging the sensitive and rapid reversible response of COFs, the COF-based respiratory monitoring system can effectively record normal breath, rapid breath, and breath apnea, enabling over a thousand cycles of hour-level continuous monitoring during daily wear. Next, we took the groundbreaking step of advancing the humidity sensor to the clinical trial stage. In clinical experiments on real sleep apnea patients, the COF-based respiratory monitoring system successfully recorded hour-level sleep respiratory data and differentiated the breathing behavior characteristics and severity of sleep apnea patients and subjects with normal sleep function and primary snoring patients. This work successfully advanced humidity sensors into clinical research for real patients and demonstrated the enormous application potential of COF materials in clinical diagnosis.

Electrospun PVDF/aromatic HBP of 4th gen based flexible and self-powered TENG for wearable energy harvesting and health monitoring

In recent times, high-performance wearable electronic devices that can transform mechanical force into electrical energy for biomedical monitoring applications are receiving an increasing amount of attention. In the present study, we focused on a flexible, self-powered and wearable triboelectric nanogenerator (TENG) based on electrospun polyvinylidene fluoride (PVDF)/aromatic hyperbranched polyester of 4th generation (Ar.HBP-G4, 0–40 wt.-% w.r.t. PVDF content) blend nanoweb as tribo-negative layer and melt-blown thermoplastic polyurethane (TPU) as tribo-positive layer for energy harvesting and human health monitoring applications. Among the varying Ar.HBP-G4 content used, incorporation of Ar.HBP-G4 (10 wt.-%) in PVDF (P-Ar.HBP-G4-10) showed higher increase in the triboelectric output voltage when compared to pristine PVDF and other Ar.HBP-G4 weight ratios. The optimized P-Ar.HBP-G4-10/TPU based TENG exhibited a peak-to-peak voltage (Vp-p) of 124.4 V under an applied load of 9.8 N and frequency 1 Hz which is superior to many other TENGs reported elsewhere. Higher triboelectric performance of P-Ar.HBP-G4 blend based TENG compared to that of neat PVDF is attributed to the effect of Ar.HBP-G4-10 in enhancing the degree of crystallinity and polar β-crystalline phase content (98.3%) in PVDF. The ability of the TENG to power up portable electronic devices is demonstrated when it is powered for 750 s while connected through a capacitor and a rectifier, and the TENG was able to operate 45 light-emitting diodes directly. Evaluation of the triboelectric output of the TENG device attached to different parts of the human body reveal significantly better output voltage and sensitivity for human health monitoring. The results of this work pave a new way to develop TENG based on P-Ar.HBP-G4 nanowebs for sustainable energy generation and wearable healthcare monitoring systems.

Noise-less hybrid nanogenerator based on flexible WPU and siloxene composite for self-powered portable and wearable electronics

The rapid growth of portable and wearable electronics demands innovative battery solutions that are both sustainable and eco-friendly. Biomechanical energy-harvesting technology is a promising approach for operating wearable electronic devices without battery dependence. In this study, a novel noise-less hybrid nanogenerator (NLHN) was developed using 3D-printed elastic resin springs and burr-shaped water-based polyurethane (water-based PU: WPU)@Siloxene nanocomposite to effectively harvest low-frequency biomechanical energy from various human movements without generating any physical noise. By rationally integrating electromagnetic generators with triboelectric nanogenerators in a sliding mode and two contact modes, the proposed NLHN exhibited excellent output performance under random low-frequency and wide-range vibrational excitations. The WPU@Siloxene was first introduced as a highly flexible positive triboelectric layer and a noise suppressor during the sliding mode operation, while elastic resin springs significantly improved the energy harvesting performance thereby suppressing the physical noise during contact-separation. The NLHN exhibited an optimal output power of 101 mW at 6 Hz and 1.5 g acceleration. Moreover, the NLHN suppressed operating noise to only 41 dB during operation and maintained a stable output performance after 50,000 cycles. The NLHN was demonstrated to function as a continuous power source for powering portable and wearable electronics, such as smart bracelets and smartphones as well as battery-free wearable ECG monitoring systems. Conclusively, the proposed NLHN has substantial potential for silent biomechanical energy harvesting as a sustainable and eco-friendly power source for portable electronics and wearable healthcare sensing devices.

Rationale and Design of a Wearable Cardiopulmonary Monitoring System for Improving the Efficiency of Critical Care Monitoring

Despite the recent development of wearable cardiopulmonary monitoring devices and their necessity in clinical settings, the evidence regarding their application in real-world intensive care units (ICUs) is limited. These devices have notable problems, such as inefficient manufacturing and cumbersome hardware for medical staff and patients. In this study, we propose a simplified cardiopulmonary monitoring system and present a protocol for a single-center prospective study to evaluate the efficacy of the proposed system compared with those from the conventional monitoring system. The system was designed to continuously measure electrocardiogram, respiration rate, and oxygen saturation in a stand-alone device with an intuitive data visualization platform and automatic data collection. The accuracy of the data measured from the proposed device will be pre-validated by comparing them with those from the reference device. Medical staff from the St. Vincent’s Hospital ICU will complete a five-point Likert-type scale questionnaire regarding their experience with conventional ICU monitoring systems. The result will be compared with the second questionnaire conducted after deploying the system. Since this is a study proposal paper, we do not have any data on this study yet. However, compared with the conventional patient monitoring system, the proposed device should be a promising method to relieve medical staff fatigue and that of the patients who must wear and attach the monitoring device for a long time.

Advancements in Wearable EEG Technology for Improved Home-Based Sleep Monitoring and Assessment: A Review.

Sleep is a fundamental aspect of daily life, profoundly impacting mental and emotional well-being. Optimal sleep quality is vital for overall health and quality of life, yet many individuals struggle with sleep-related difficulties. In the past, polysomnography (PSG) has served as the gold standard for assessing sleep, but its bulky nature, cost, and the need for expertise has made it cumbersome for widespread use. By recognizing the need for a more accessible and user-friendly approach, wearable home monitoring systems have emerged. EEG technology plays a pivotal role in sleep monitoring, as it captures crucial brain activity data during sleep and serves as a primary indicator of sleep stages and disorders. This review provides an overview of the most recent advancements in wearable sleep monitoring leveraging EEG technology. We summarize the latest EEG devices and systems available in the scientific literature, highlighting their design, form factors, materials, and methods of sleep assessment. By exploring these developments, we aim to offer insights into cutting-edge technologies, shedding light on wearable EEG sensors for advanced at-home sleep monitoring and assessment. This comprehensive review contributes to a broader perspective on enhancing sleep quality and overall health using wearable EEG sensors.

Exploration on flexible wearable sensor motion monitoring based on novel functional polymer conjugated materials.

With the continuous development of flexible electronics, multi-functional device integration, artificial intelligence technology, etc., flexible wearable sensors are playing an increasingly important role in people's daily motion monitoring. However, current flexible wearable sensors have problems such as low accuracy, poor real-time performance, and poor stability in motion monitoring, which seriously hinder the better application of flexible wearable sensors and are not conducive to the collection and monitoring of motion signals. To this end, this paper designed a flexible wearable sensor motion monitoring system and tested its performance through the preparation and performance research of new functional polymer conjugated materials. The research results show that the motion monitoring system designed based on the new functional polymer conjugated material flexible wearable sensor has good monitoring accuracy and real-time performance. When the output data is 500 pieces, the running monitoring accuracy reaches 95.4%, and the monitoring feedback time is 0.321s; the high jump movement monitoring accuracy rate reaches 97%, and the monitoring feedback time is 0.287s; the long jump movement monitoring accuracy rate reaches 96%, and the monitoring feedback time is 0.296s. This shows that the flexible wearable sensor motion monitoring system of this paper has better performance and can meet the current demand for accuracy and real-time motion monitoring. This study highlights the impact of new functional polymer conjugated materials on flexible wearable sensors, helping to further solve the deficiencies of flexible wearable sensors in sports monitoring and promote their better development.

Low-cost carbon dioxide concentration sensors for assessing air quality in the built environment: an on-site evaluation of their measurement performance

Carbon dioxide (CO2) concentration is commonly used to determine indoor air quality. However, wearable environmental monitoring devices have not considered CO2 content, because they have mainly focused on the outdoor environment. Our goal is to develop a new wearable monitoring system for indoor research purposes that also incorporates a CO2 concentration sensor. We evaluated the performance of two low-cost CO2 sensors, the Senseair K30 and the Sensirion SCD30, by comparing them with a professional reference sensor in two different environments: a Test Cell and the LabZEB of ITC-CNR. A first result shows that both low-cost sensors have acceptable performance, with the K30 showing a trend closer to that of the professional reference sensor and better overall performance. For this reason, we conducted a third test in which we encapsulated the selected K30 low-cost sensor in a 3D-printed, wearable housing that supports the sensor and control unit to see if the 3D-printed part could affect measurement performance. The result shows no interference in measurement performance. This suggests that low-cost sensors can be used in a wearable system to assess indoor air quality for environmental and human-centered research in the built environment.

Low-cost sensor for particulate matter concentration: an evaluation of its measurement performance in the field by direct comparison with a calibrated reference system

The WHO defines threshold values of PMs. Different studies where wearables are used also to monitor PMs. With the main goal of developing a new wearable monitoring system for research purposes based on low-cost sensors and open-source hardware and considering the wide variety of low-cost sensors available on the market today, we focused on evaluating the measurement performance of a particular type of low-cost PMS, the Plantower PMS A003I. It was connected to an Arduino Micro, which served as the microcontroller and data logger. The low-cost system was used in a direct comparison with a calibrated DustTrak DRX as the reference instrument. The test was conducted in a real office in four different sections of monitoring activity on four different days. The result of the proposed method suggests that it is possible to use low-cost sensors to evaluate indoor air quality in terms of PM levels.

SISTINE 3.0: Wearable System for Remote Functional Assessment of Phlebopathics.

Phlebopathic diseases are chronic conditions that impact the health status and affect functional capacity. We developed SISTINE 3.0, a wearable system for remote monitoring of patients, and the aim of the study is to evaluate whether it can detect differences between participants with phlebology and healthy ones. Twelve patients and five healthy subjects performed a 3-metres Timed-Up-Go wearing SISTINE 3.0 system. The results support the system's potential to discriminate participants, especially based on the linear walking and turning angular velocity.

Enhancing Wearable Gait Monitoring Systems: Identifying Optimal Kinematic Inputs in Typical Adolescents.

Machine learning-based gait systems facilitate the real-time control of gait assistive technologies in neurological conditions. Improving such systems needs the identification of kinematic signals from inertial measurement unit wearables (IMUs) that are robust across different walking conditions without extensive data processing. We quantify changes in two kinematic signals, acceleration and angular velocity, from IMUs worn on the frontal plane of bilateral shanks and thighs in 30 adolescents (8-18 years) on a treadmills and outdoor overground walking at three different speeds (self-selected, slow, and fast). Primary curve-based analyses included similarity analyses such as cosine, Euclidean distance, Poincare analysis, and a newly defined bilateral symmetry dissimilarity test (BSDT). Analysis indicated that superior-inferior shank acceleration (SI shank Acc) and medial-lateral shank angular velocity (ML shank AV) demonstrated no differences to the control signal in BSDT, indicating the least variability across the different walking conditions. Both SI shank Acc and ML shank AV were also robust in Poincare analysis. Secondary parameter-based similarity analyses with conventional spatiotemporal gait parameters were also performed. This normative dataset of walking reports raw signal kinematics that demonstrate the least to most variability in switching between treadmill and outdoor walking to help guide future machine learning models to assist gait in pediatric neurological conditions.

RECOGNITION OF ATRIAL FIBRILLATION BASED ON CNN-LSTM AND LAPLACIAN SUPPORT VECTOR MACHINE

Atrial fibrillation (AF) is a common arrhythmia associated with cardiac death and stroke. Therefore, the early detection of AF is of critical importance and the wearable long-term ECG monitoring system is one promising way. In order to assist the cardiologists in identifying potential AF in a tremendous amount of long-term ECG data, this study proposed an automatic detector combining deep learning and semi-supervised learning in view of the difficulty of obtaining a large number of labeled data in clinical practice. Three R-R interval features and two nonlinear features extracted from ECG samples, combined with 16-dimension deep learning features extracted by CNN-LSTM, are put into the semi-supervised machine learning model Laplacian Support Vector Machine (LapSVM) for AF detection. The proposed method showed very promising performance, with an accuracy of 99.63%, a sensitivity of 99.70%, a specificity of 99.57% and an F_score of 99.59% on the AFDB dataset. It still achieved an accuracy of 98% when the proportion of the training set was reduced, and achieved an accuracy of 96% on the SPHD collected clinically. The results show that the proposed method can classify AF and non-AF with a higher accuracy, and has excellent generalization performance in different categories of subjects, which is in line with clinical scenarios. The proposed method is also conducive to solving the clinical cases with little labeled data.

Ultrathin Self-Powered Heavy-Metal-Free Cu-In-Se Quantum Dot Photodetectors for Wearable Health Monitoring.

Mechanically deformable photodetectors (PDs) are key device components for wearable health monitoring systems based on photoplethysmography (PPG). Achieving high detectivity, fast response time, and an ultrathin form factor in the PD is highly needed for next-generation wearable PPG systems. Self-powered operation without a bulky power-supply unit is also beneficial for point-of-care application. Here, we propose ultrathin self-powered PDs using heavy-metal-free Cu-In-Se quantum dots (QDs), which enable high-performance wearable PPG systems. Although the light-absorbing QD layer is extremely thin (∼40 nm), the developed PD exhibits excellent performance (specific detectivity: 2.10 × 1012 Jones, linear dynamic range: 102 dB, and spectral range: 250-1050 nm at zero bias), which is comparable to that of conventional rigid QD-PDs employing thick Pb-chalcogenide QD layers. This is attributed to material and device strategies─materials that include Cu-In-Se QDs, a MoS2-nanosheet-blended poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) hole transport layer, a ZnO nanoparticle electron transport layer, Ag and ITO electrodes, and an ultrathin form factor (∼120 nm except the electrodes) that enable excellent mechanical deformability. These allow the successful application of QD-PDs to a wearable system for real-time PPG monitoring, expanding their potential in the field of mobile bioelectronics.

Wearable Sleep Monitoring System Based on Machine Learning Using Snoring Sound Signal

Abstract According to the obstructive sleep apnea Syndrome (OSAS), a wearable sleep monitoring system is designed based on machine learning using snoring sound signal. The system picks up snoring signal via bone conduction sensor, and calculates the apnea-hypopnea index (AHI). By analyzing the snoring signal in frequency domain, spectral entropy and other frequency-domain features are selected. Finally, the neural network classifier model is established. In the model, the input variables are eight frequency-domain features, and the output response is related to AHI value. Trained by machine learning, the result shows that the average accuracy in identifying the severity of the four kinds of OSAS categories is 59%. The system uses the measured data of snoring to analyze the symptoms of OSAS, so as to realize the preliminary forecast based on the snoring data. The system proposed in this paper has a good application development prospect in intelligent monitoring and medical instruments.

Uneven terrain affects metabolic cost and gait in simulated complex lunar surfaces

Objective. Upcoming missions of the National Aeronautics and Space Administration (NASA) to the Moon will include extensive human exploration of the lunar surface. Walking will be essential for many exploration tasks, and metabolic cost during ambulation on simulated complex lunar surfaces requires further characterization. In this study, ten healthy subjects (6 male and 4 female) participated in three simulated lunar terrain walking conditions at the NASA Johnson Space Center’s planetary ‘Rock Yard’: (1) flat terrain, (2) flat terrain with obstacles, and (3) mixed terrain. Approach. Energy expenditure and gait were quantified with a wearable metabolic energy expenditure monitoring system and body-worn inertial measurement units (IMUs), respectively. Main results. It was found that participants walking on the mixed terrain, representing the highest workload condition, required significantly higher metabolic costs than in other terrain conditions (p < 0.001). Additionally, our novel IMU-based gait variables discriminated different terrains and identified changes in gait in simulated lunar terrain environments. Significance. Our results showed that the various surface irregularities and inconsistencies could cause additional physical effort while walking on the complex terrain. These findings provide insight into the effects of terrain on metabolic energy expenditure during simulated lunar extravehicular activities.

A Wearable Joint Moment Real-Time Monitoring System: Joint Moment Analysis and Injury Prevention in Human Activities

A Wearable Joint Moment Real-Time Monitoring System: Joint Moment Analysis and Injury Prevention in Human Activities

Machine learning-based smart wearable system for cardiac arrest monitoring using hybrid computing

Every year, the percentage of people affected by cardiovascular diseases increases drastically. Out of them, a heart attack is the most prominent and painful disease. According to the World Health Organization, approximately 17.5 million people lose their lives yearly due to this disease, which is alarming. The remarkable advancement in wearable technology has opened doors to propose many effective smart solutions to tackle this disease efficiently. Furthermore, early diagnosis of heart attack proliferates the compatibility of meditation and expedites the diagnostic recommendation by clinical experts. Considering this problem’s sensitivity, we proposed a wearable smart and early Heart Attack diagnosis system and adopted a decentralized computational phenomenon using hybrid computing architecture. It reflects better response time and minimal latency to detect a heart attack in its preliminary stage for homage patients. The proposed system can monitor and trigger the patient current heart status classified on required heart diagnosis parametric sensors assembled on the patient’s body with the help of an android application. In this study, three models are developed using the Support Vector Machine (SVM), Adaptive Boosting (AdaBoost), and Random Forest (RF) algorithms for the classification. Performance measures: accuracy, error rate, and response time are used to evaluate the proposed system. Our research findings promise that it can be implemented on patients diagnosed with the risk of a heart attack to monitor their heart health remotely and prevent sudden heart failure without impeding a person’s everyday life.

AI-enabled epidermal electronic system to automatically monitor a prognostic parameter for hypertension with a smartphone

We present a wearable, flexible, wireless and smartphone-enabled epidermal electronic system (EES) for the continuous monitoring of a prognostic parameter for hypertension. The thin and lightweight EES can be tightly attached to the chest of a patient and synchronously monitor first lead electrocardiograms (ECG) and seismocardiograms (SCG). To demonstrate the concept, we developed the EES using state-of-the-art cleanroom technologies. Two types of sensors were integrated: A pair of metal electrodes to contact the skin and to record ECG and a vibration sensor based on a thin piezoelectric polymer to record SCG from the same location of the chest, simultaneously. The complete EES was powered by the near field communication functionality of the smartphone. We developed a machine-learning algorithm and trained it on public ECG data and recorded SCG signals to extract characteristic features of the recordings. Binary classifiers were used to automatically annotate peaks. After training, the algorithm was transferred to the smartphone to continuously analyze the timing between particular ECG and SCG peaks and to extract the Weissler’s index as a prognostic parameter for hypertension. Tests with data of healthy control persons and clinical experiments with patients diagnosed with cardio-pulmonary hypertension showed a promising prognostic performance. The presented EES technology could be utilized for pre-screening of cardio-pulmonary hypertension, which is a strong burden in our today’s healthcare system.